James Ruppert Jr.

(Penn State Assistant Research Professor)

The Critical Role of Cloud–Infrared Radiation Feedback in Tropical Cyclone Development

What
When Feb 05, 2020
from 03:30 pm to 04:30 pm
Where 112 Walker Building, John Cahir Auditorium
Contact Name Steven Feldstein
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James Ruppert Jr

The Critical Role of Cloud–Infrared Radiation Feedback in Tropical Cyclone Development

At present, there is little understanding and hence poor awareness of the potential role of radiative feedbacks in the development of tropical cyclones (TCs).  To the extent that such feedbacks are important to TC genesis and intensification, this highlights a potentially dangerously gap in our understanding, especially given that model forecasts of TC intensity have remained inadequate and largely unimproved for several decades running.  Here we seek to test the importance of these feedbacks in realistic scenarios by conducting a set of large-domain convection-resolving WRF simulations of Super Typhoon Haiyan (2013) and Major Hurricane Maria (2017).  In both storms, we find that the interaction between clouds and infrared or longwave radiation is critical to their intensification, and is therefore critical to the intensity that they both ultimately reach prior to landfall.  The underlying driver of this radiative feedback is the cloud greenhouse effect – the anomalous longwave warming in the boundary layer–midtroposphere in regions of deep convection caused by optically opaque cloud.  Diagnosis using the Sawyer–Eliassen equation (SEQ) indicates that this cloud–longwave heating signature enhances the transverse or secondary circulation in the precursor storm by ~10% or greater, thus accelerating its development.  These results have potential implications for the number of strong TCs we experience globally each year.  The findings also indicate the importance of addressing forecast model errors in the representation of cloud microphysics and their interaction with radiation.  It is at least possible that doing so will break the longstanding deadlock in TC intensity forecasting.